Increasing Demand in a Flat-Growth Environment

Economic growth in the US continues to face many daunting challenges. Companies across a wide range of industry sectors are experiencing top-line sales growth that is anemic at best, and in many cases negative. Foodservice is no exception: belt-tightening by households certainly impacts the food away from home sector. In the absence of the natural demand increase provided by a growing economy, what can enterprises do to improve their top-line performance?

certain products go together

At Sentrana we believe that companies can increase sales, even in tough economies, by understanding their own demand environments at the most detailed level possible – in other words, to be able to predict what products to offer to what customers, and to use insights from available sales data to make targeted recommendations around pricing, promotional activities and timing. In foodservice hundreds of thousands of products pass through any given distribution channel every day to hundreds of thousands of restaurants and other operators. To meet this challenge effectively manufacturers and distributors need to contribute their respective insights about products and customers onto a common platform from which to obtain a full picture of demand. Recently this has motivated prominent industry players to collaborate in managing performance across key product categories.

Manufacturers and distributors approach the growth challenge in different ways. For distributors the goal is to grow sales in the category across all products and brands; while for manufacturers the key goal is to sell their own brands at the expense of those of their competitors. At first glance it may seem like these goals are at cross-purposes. If a collaborative category management program helps the distributor capture a sale that would otherwise have been made by a different wholesaler, then that distributor generates income it otherwise would not gain. From a manufacturer’s perspective, however, this may amount to little more than channel shift – the same case of tomato sauce, say, being sold by Distributor A rather than Distributor B, and thus not a net gain to the manufacturer’s own income statement.

Despite these different goals there is a way for category management to lead both manufacturers and distributors to direct financial benefits, not merely demand shift. Consider the case of tomato sauce we used as an example above. Now, at any point in time a single manufacturer – call it Manufacturer A – has a certain market share for each product it sells. The end customer – the foodservice operator – may be buying Manufacturer A’s brand or it may be buying a competing brand. Over any defined market (e.g. regional sales territory) the incidence of purchase of Manufacturer A’s brand should be equal to this manufacturer’s share of the market.

Let’s focus first on what is happening at the distributor level. The distributor’s goal – call it Distributor A – in this scenario is to create conditions by which an end customer will want to buy a certain product from Distributor A that the customer now buys from somewhere else. That is understandable in the abstract, but in the real world how is Distributor A supposed to know which customer to approach, which product to offer, and the terms at which to make the offer such that it will be attractive to the customer to shift purchase?

The answer to this involves a technical term – association and classification modeling – and a more reader-friendly explanation: certain products go together. The distributor’s sales data may identify 100 customers who have recently purchased prepared pizza crusts, tomato sauce and mozzarella. If the 101st customer recently purchased pizza crusts and mozzarella, it is a reasonable prediction that the customer is purchasing tomato sauce from somewhere else. The models we referred to above spot this opportunity and alert the relevant decision makers. We have homed in on which product to offer to which customer.

We still have a problem, though. We have identified the opportunity at the product level – tomato sauce – but do we know enough about the customer to understand his or her preferences within that product area? From the distributor’s perspective the answer is probably: no. The distributor’s job is to move product, not to be deeply familiar with the qualities and attributes of individual brands and SKUs. So now we must move the focus upstream to the manufacturer, who does possess that deep brand knowledge. Manufacturer A can tell us what product attributes may be most attractive to the customer to whom we are trying to sell the tomato sauce. This helps Distributor A move to a further level of granularity and identify which SKU/s, out of all the possibly hundreds that exist in the tomato sauce classification, may be the most likely to induce the customer to switch from their present distributor. Manufacturer A can even provide supporting sales collateral like recipes and usage suggestions to help Distributor A’s sales representatives close the deal.

Now we come to the real value proposition for the manufacturer. What has transpired in the scenario we described above is that a sale of any tomato sauce by any distributor has become a sale of a specific tomato sauce SKU to a deliberately targeted customer. The sale of “any tomato sauce” may have involved one of Manufacturer A’s brands or it may have involved a competitor’s brand – in aggregate, as noted above, this would be in proportion to Manufacturer A’s market share. For every instance where the customer would otherwise have purchased a competing brand, the sale of a targeted SKU through Distributor A results in incremental sales growth for Manufacturer A. Not demand shift, but real incremental growth.

Not every opportunity will be realized, of course. There will be plenty of occasions when, for whatever reason, the end customer is not convinced to make the switch and continues to buy through the current distributor. In our experience, though, robust predictive technology contributes a significant positive impact with the potential to enjoy success rates well in excess of traditional penetration campaigns. In foodservice, manufacturers and distributors are only just beginning to realize the potential benefits of collaboration and establish platforms to leverage their respective contributions. With the economic landscape continuing to look challenged for the near to intermediate term, the timing could hardly be more fitting for taking this collaboration to the next level.

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Managing the Category Beyond SKU Rationalization

SKU proliferation has been a fact of life in foodservice much as it has been in other industries in recent years. Proliferation creates considerable pressure throughout the value chain to make tough decisions about SKU assortment across numerous product categories. In foodservice the problem is not shelf space as it is in retail; rather, it is the limited amount of product information that a sales representative can manage in his or her head in order to match the right products with the right customers on a daily basis in real time. As managing assortment has grown more complex, manufacturers and their downstream partners have looked to SKU rationalization to reduce streamline product offerings and manage inventory costs for improved category performance. While SKU rationalization can address these challenges to some extent, it does not get to the core of the problem. The most effective way to improve category performance is to increase demand for products in that category. In turn, the best way to grow demand is to seamlessly match unique customers with the products whose attributes they most highly value. This requires a holistic category management approach, supported by robust data analytics that can take into account the key levers of demand – assortment, promotions, pricing and purchase timing.

The Importance of Collaboration

In foodservice, manufacturers and distributors are the logical partners for a collaborative category management venture. Manufacturers possess deep insights into the product attributes that drive demand for specific customer types, and have a strong understanding of how to manage assortment. On the other side, distributors have the benefit of daily transaction data at a very granular level – what quantities of products in the category are being sold to what locations with what frequency. Combining these insights – ideally through a single integrated data management system able to process inputs from multiple sources and generate insights and actionable recommendations to the relevant decision makers – can create a coherent, unified picture of demand that provides a basis for specific assortment, pricing and promotional activities to grow sales.

Reducing the Guess Factor

A traditional SKU rationalization program may analyze aggregate transaction histories for all the SKUs in a category and mark for elimination some subset of those that occupy the so-called “long tail” – products with sparse data records due to infrequent activity. A typical goal in this regard may be to eliminate 20-25% of all SKUs in the category. The problem with this approach is that without an appropriately detailed level of analytical insight, managers are left to guessing what the resulting effects will be on sales. Transaction frequency is only one variable in presenting a composite picture of demand. For example a certain product may transact on an infrequent basis only, but it may also be a popular niche product with attributes highly valued by major customers. What will the sales impact be of not having this niche product available when a major customer wants to add it to his or her market basket? How can decision makers recognize and differentiate between niche products and other long tail denizens that really deserve to be eliminated from the active product line?

A holistic category management solution, driven by advanced predictive science, can supply answers to these questions. By integrating product attribute knowledge possessed by the manufacturer with quantity and purchase timing data known by the distributor, the system can make recommendations about when to stock the low-frequency but desirable niche items with a higher likelihood of coincidence with the customer’s purchase decision. Techniques such as Hierarchical Bayesian modeling help overcome the analytical challenges typically presented by sparse data. Rather than losing all or part of the customer’s market basket for the sake of an incremental SKU reduction – in most likelihood a losing proposition – the result is retaining a satisfied customer.

Focus on Growing Demand

This approach to category management program shifts attention away from simple cost reduction through inventory rationalization and focuses instead on the revenue side of the equation – growing demand in the category. There are two critical requirements for this to be successful. First, the data management platform must be sufficiently granular to provide meaningful insights at the level of every customer and every product (for example as in the long tail analysis described above). Second, the platform must seamlessly transform into a practical tool which sales representatives can use in the field. This is a particularly important requirement. Foodservice sales & marketing representatives as a rule have very little time for incremental effort above and beyond their existing selling and administrative responsibilities. They certainly do not have sufficient time to juggle multiple sales tools offered by multiple manufacturers acting in the role of category manager. The ideal tool is one with which the representatives have existing familiarity (to avoid time-consuming learning curves for new processes) and which can seamlessly integrate data from multiple input sources.

Manufacturers and distributors need more than just a rationalization program to optimize performance at the category level. A holistic approach, supported by robust analytics delivering actionable real-time guidance to sales professionals in the field, can improve category performance all along the foodservice value chain.

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Analytics for Intelligent Category Management

Collaboration between distributors and manufacturers is the cornerstone of category management in foodservice. For a given product category a manufacturer is selected to be category captain, with responsibility for improving category performance. This post addresses some key data and analytical issues with which manufacturers should expect to deal as category captains.

So you have been asked by your most important foodservice distribution partner to be a category captain. What happens next? As captain you are tasked with managing the assigned category for optimal performance. That entails the following:

•    Analyze all products across the category (not just your own brands)
•    Augment the data provided by the distribution partner with your own internally generated insights
•    Provide structured, actionable recommendations based on intelligence obtained from the data

These recommendations relate to product assortment, pricing policies, promotional activities and other important demand levers for driving profitability. At the same time you need to educate your distribution partners, both at corporate headquarters and in the field, about the product characteristics that can help increase demand. This requires an intelligent approach to data analytics.

What insights about products can help drive category sales?

What might a good analytics model for category management look like? Let’s consider the key tasks we identified in the previous paragraph.

Analyze All Products Across Category

Category management is driven by analytics. As category captain you will receive transaction data from your distribution partner to form the basis of your insights and recommendations. The first issue with which you will likely have to deal is the quality and completeness of this information. Bear in mind that foodservice distributors are typically not used to sharing sensitive sales data with their suppliers, and may lack effective internal processes for making it available. Robust data management solutions like Sentrana’s MarketMover™ help collaborative category partners overcome this challenge by providing timely access to clean, customer-level data.

The next order of business is to map out the analytical processes that can best support your distribution partner’s objective to improve category demand. This may be best approached through posing a series of questions. For example:

•    What intelligence can we derive from the data to help identify ways to improve product demand among existing customers?
•    What patterns and associations will provide us insights about products that current customers are not buying from our distribution partner but could be enticed to buy?
•    How can we improve sales turnover by encouraging customers to switch from lower-to higher-velocity SKUs?

Augmenting Data with Internally Generated Insights

In answering those and similar questions one of your most important activities is to augment the data your distribution partner provides with your own unique insights about the products in the category. An important example of this are the product attributes that drive demand among certain customer types. Perhaps you are charged with managing baked goods and you need to figure out what the right use of shelf space will be for muffin products. Your distribution partner’s objective is to increase total muffin sales – for example along the lines of one of those three questions posed in the previous paragraph. As a manufacturer you can provide your own deep knowledge about what features and attributes drive sales among certain customers.

A critical data challenge, therefore, is to have the ability to map specific attributes to specific products. Category managers should be able to access the product database and establish product groupings and categories based on like attributes. Using the above example, for every product you can assign a  quantitative attribute metric. “Butteriness” may be an appropriate attribute for muffins, and you can rank all applicable products along the lines of “very / moderately / not very buttery”. This can facilitate more rational product groupings within the category that better enables you to analyze and evaluate assortment trade-offs, pricing strategies and promotional approaches.

This kind of product administration capability brings up in turn a whole series of issues around how to create standardized attribute definitions for each relevant subcategory and product set. By allowing category mangers to create new product and subcategory groupings, it becomes likely that these categories will not map directly to those of the distributor. A category administration functionality is required that will manage the interface between the distribution taxonomy and the specific product and attribute groupings mapped by category managers at the manufacturer.

Supporting Analytics

As you map out these processes you can get a better sense of what the analytical capabilities may look like. For example, what data exploration functionalities can help you analyze effectively? To orient your own understanding of the structure of subcategories and products to its organization in your distribution partner’s data records you need a mechanism for guided drill-down and drill-up within products, as well as in regard to customers, sales territories and other key information. There should be a filtering mechanism that allows you to view products along a number of descriptors: for example, all the SKUs currently stocked at a particular operating company of your distribution partner, or all products with the item description “frozen”.

Visualization and editing capabilities are also important components of analysis. How do you want to see the information and organize it into compelling formats for your targeted readers? You will probably want to have a variety of formats and the tools to manipulate data into different visual representations to underscore the insights about customers and products you wish to communicate. You will also want to be able to easily access and modify the reports and formats you use most frequently, and to share reporting and editing capabilities with others working on the same projects.

Providing Guidance and Recommendations

Category managers need to consider how best to translate analytical insights into actionable recommendations for their partners. For example, developing strong promotional content around products for the distributor’s sales force can be an important way to execute against category performance targets. A system for uploading, managing and exporting product-related content is thus an important functionality to consider. Another valuable feature could be scenario analysis capabilities to map out alternative approaches to pricing decisions, promotional opportunities and assortment trade-offs. Finally, manufacturers need to consider how to incorporate data from their own market sources: for example sales information at the total market level rather than just the share occupied by their distribution partner.

Collaborative category management can evolve into a long-term relationship that will improve category performance for distributors and improve overall product sales for manufacturers. Over time the scope of a category management program may expand to include enhanced predictive initiatives and a fuller set of demand levers. Building a good foundation with the right data analytics is a good place to start.

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Crunch the Numbers that Really Matter (hint:they’re the ones that relate to downstream demand)

A New Approach to Trade Spend for Foodservice Manufacturers

There is no shortage of quantitative analysis in the trade spend practices of foodservice manufacturers.  Unfortunately, very little of this analysis helps give decision-makers insights about the effectiveness of their trade spend programs.  The numbers being crunched do not relate to signals about actual downstream demand, but rather to the formidable mountain of claims from their distributors.  These claims come in all manner of data formats and accounting entries and it typically takes armies of brokers, salespeople and financial staff to figure them out.  After all the cumbersome and error-prone line-by-line calculations to validate claims are said and done, you are no more informed about the profitability or the potential risks associated with any given program.  No wonder there is widespread dissatisfaction with the effectiveness of these programs.  Over 75% of manufacturers in this sector consider their trade spend initiatives to be inefficient, according to the 2010 MarketIntelligence Foodservice Trade Survey. Read the rest of this entry »

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Missing the Ocean for the Stream: What We Can and Cannot Learn from IBM’s New Breakthrough

As part of its perpetual quest to reinvent and perfect its business model, IBM has made an aggressive push into the analytics market in the last half-dozen or so years. The company’s slick, though occasionally confusing ad campaigns (remember those ads with the mysterious red box being unveiled?) often announce its new initiatives, though it is not always clear that a new announcement is indeed a major one. In the analytics space, however, Big Blue does mean business. The announcement of its sizable new business analytics and optimization division is clearly intended to prove as much. Shortly after its announcement, IBM also unveiled a new stream computing platform called “System S” to much fanfare. The breathless enthusiasm of business journalists, technology bloggers and investment analysts has been palpable. But what exactly does this technological advancement do, and what does it mean for your business?

To answer this question, let’s begin briefly by dissecting what IBM has introduced. Imagine that you are receiving a continuous stream of data, such as stock prices on the Nasdaq. These figures must be quickly analyzed so that the proper buy and sell orders can be placed. Suppose that you also need to base your decisions not just on the Nasdaq prices but also the numbers figures coming in from dozens of other exchanges. Read the rest of this entry »

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